2021
DOI: 10.3390/rs13214385
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Empirical Orthogonal Function Analysis and Modeling of Global Tropospheric Delay Spherical Harmonic Coefficients

Abstract: Based on the ERA-5 meteorological data from 2015 to 2019, we establish the global tropospheric delay spherical harmonic (SH) coefficients set called the SH_set and develop the global tropospheric delay SH coefficients empirical model called EGtrop using the empirical orthogonal function (EOF) method and periodic functions. We apply tropospheric delay derived from IGS stations not involved in modeling as reference data for validating the dataset, and statistical results indicate that the global mean Bias of the… Show more

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Cited by 6 publications
(2 citation statements)
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“…In other words, possible spatial modes of variability (patterns of variability) in climate studies can often be studied by EOF analysis. It is used for decomposing the time series data set into mutually independent spatial and temporal function parts to explain the spatiotemporal variability with the least possible modes [29]. The eigenvectors are the space function part which is composed of several mutually independent and orthogonal space modes [30].…”
Section: Empirical Orthogonal Functionmentioning
confidence: 99%
“…In other words, possible spatial modes of variability (patterns of variability) in climate studies can often be studied by EOF analysis. It is used for decomposing the time series data set into mutually independent spatial and temporal function parts to explain the spatiotemporal variability with the least possible modes [29]. The eigenvectors are the space function part which is composed of several mutually independent and orthogonal space modes [30].…”
Section: Empirical Orthogonal Functionmentioning
confidence: 99%
“…In other words, possible spatial modes of variability (patterns of variability) in climate studies can often be studied by EOF analysis. This technique decomposes the time series dataset into spatial and temporal components, to get a better understanding of the variability with minimal modes required [32]. In a space function, the eigenvectors are composed of several orthogonal space modes, each of which is mutually independent [11].…”
Section: Empirical Orthogonal Functionmentioning
confidence: 99%